# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic word2vec example."""
#coding:utf-8 
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
from tempfile import gettempdir
import zipfile
import json
import numpy as np
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
#导入mpl
from pylab import mpl 
#修改ubuntu字体用来兼容中文显示
zhfont = mpl.font_manager.FontProperties(fname='/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc')


#读入全宋词
file_object = open('/home/ai/work/W11/quiz-w10-code/QuanSongCi.txt').read()
# Read the data into a list of strings.
def read_data(filename):
  data = tf.compat.as_str(file_object)
  return data

vocabulary = read_data(file_object)#读文件，获取所有字的列表，在这里没做去掉词牌名等操作，本人认为这也是一种有效信息，所以不做删除操作
print('Data size', len(vocabulary))
print(vocabulary)

# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 5000#保留前5000个高频词

#建立数据字典
def build_dataset(words, n_words):
  """Process raw inputs into a dataset."""
  count = [['UNK', -1]]#二维列表，用于计数每个词出现的次数
  count.extend(collections.Counter(words).most_common(n_words - 1))#把前5000个高频字存入count中
  dictionary = dict() #一个空字典，按频率从高到低的顺序，先后存放5000个高频字
  for word, _ in count:#顺序遍历count，依次存入字典中，key为值，value为序号(即出现频率的排名)
    dictionary[word] = len(dictionary)
  data = list() #一个空list，用于存放原始数据字符串中，每个字在字典里的位置
  unk_count = 0
  for word in words:# 遍历原始数据串，获得其在dict中位置，默认为0(即未出现在dict中的低频字)
    index = dictionary.get(word, 0)
    if index == 0:  # dictionary['UNK']
      unk_count += 1    #未出现，低频字累加计数
    data.append(index)  #出现，放入data的list中，
  count[0][1] = unk_count  #结果存入count中的'UNK'项
  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys())) #反转字典
  return data, count, dictionary, reversed_dictionary

# Filling 4 global variables:
# data - list of codes (integers from 0 to vocabulary_size-1).
#   This is the original text but words are replaced by their codes
# count - map of words(strings) to count of occurrences
# dictionary - map of words(strings) to their codes(integers)
# reverse_dictionary - maps codes(integers) to words(strings)
data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,
                                                            vocabulary_size)
#导出文字索引字典
with open('./dictionary.json', "w") as f:
  jsObj = json.dumps(dictionary,ensure_ascii=False,indent=2)
  f.write(jsObj)
  f.close()

with open('./reverse_dictionary.json', "w") as f:
  jsObj = json.dumps(reverse_dictionary,ensure_ascii=False,indent=2)
  f.write(jsObj)
  f.close()

del vocabulary  # Hint to reduce memory.  # 删除原始数据字符串，节约内存.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0

# Step 3: Function to generate a training batch for the skip-gram model.
#batch_size为batch大小，num_skips为对每个单词生成样本数，skip_window为单词最远可以联系的距离
def generate_batch(batch_size, num_skips, skip_window):
  global data_index
  assert batch_size % num_skips == 0#断言batch_size是num_skips的整倍数
  assert num_skips <= 2 * skip_window#断言num_skips不大于skip_window的两倍
  batch = np.ndarray(shape=(batch_size), dtype=np.int32)
  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
  span = 2 * skip_window + 1  # [ skip_window target skip_window ] #对某个单词创建相关样本时会使用到的单词数量
  buffer = collections.deque(maxlen=span)#创建最大容量为span的队列，即双向队列
  if data_index + span > len(data):
    data_index = 0
  buffer.extend(data[data_index:data_index + span])
  data_index += span
  for i in range(batch_size // num_skips):
    context_words = [w for w in range(span) if w != skip_window]
    words_to_use = random.sample(context_words, num_skips)
    for j, context_word in enumerate(words_to_use):
      batch[i * num_skips + j] = buffer[skip_window]
      labels[i * num_skips + j, 0] = buffer[context_word]
    if data_index == len(data):
      #buffer[:] = data[:span]
      for word in data[:span]:
        buffer.append(word)
      data_index = span
    else:
      buffer.append(data[data_index])
      data_index += 1
  # Backtrack a little bit to avoid skipping words in the end of a batch
  data_index = (data_index + len(data) - span) % len(data)
  return batch, labels

batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
  print(batch[i], reverse_dictionary[batch[i]],
        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.

batch_size = 128
embedding_size = 128  # Dimension of the embedding vector.#将单词转为稠密向量的维度.
skip_window = 1       # How many words to consider left and right.
num_skips = 2         # How many times to reuse an input to generate a label.
num_sampled = 64      # Number of negative examples to sample.

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent. These 3 variables are used only for
# displaying model accuracy, they don't affect calculation.
valid_size = 16     # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)


graph = tf.Graph()

with graph.as_default():

  # Input data.
  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

  # Ops and variables pinned to the CPU because of missing GPU implementation
  with tf.device('/cpu:0'):
    # Look up embeddings for inputs.
    embeddings = tf.Variable(
        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
    embed = tf.nn.embedding_lookup(embeddings, train_inputs)

    # Construct the variables for the NCE loss
    nce_weights = tf.Variable(
        tf.truncated_normal([vocabulary_size, embedding_size],
                            stddev=1.0 / math.sqrt(embedding_size)))
    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

  # Compute the average NCE loss for the batch.
  # tf.nce_loss automatically draws a new sample of the negative labels each
  # time we evaluate the loss.
  # Explanation of the meaning of NCE loss:
  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
  loss = tf.reduce_mean(
      tf.nn.nce_loss(weights=nce_weights,
                     biases=nce_biases,
                     labels=train_labels,
                     inputs=embed,
                     num_sampled=num_sampled,
                     num_classes=vocabulary_size))

  # Construct the SGD optimizer using a learning rate of 1.0.
  optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

  # Compute the cosine similarity between minibatch examples and all embeddings.
  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
  normalized_embeddings = embeddings / norm
  valid_embeddings = tf.nn.embedding_lookup(
      normalized_embeddings, valid_dataset)
  similarity = tf.matmul(
      valid_embeddings, normalized_embeddings, transpose_b=True)

  # Add variable initializer.
  init = tf.global_variables_initializer()

# Step 5: Begin training.
#训练400万次
#为节约时间，运行中为40万
num_steps = 4000001

with tf.Session(graph=graph) as session:
  # We must initialize all variables before we use them.
  init.run()
  print('Initialized')

  average_loss = 0
  for step in xrange(num_steps):
    batch_inputs, batch_labels = generate_batch(
        batch_size, num_skips, skip_window)
    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

    # We perform one update step by evaluating the optimizer op (including it
    # in the list of returned values for session.run()
    _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
    average_loss += loss_val

    if step % 2000 == 0:
      if step > 0:
        average_loss /= 2000
      # The average loss is an estimate of the loss over the last 2000 batches.
      print('Average loss at step ', step, ': ', average_loss)
      average_loss = 0

    # Note that this is expensive (~20% slowdown if computed every 500 steps)
    if step % 10000 == 0:
      sim = similarity.eval()
      for i in xrange(valid_size):
        valid_word = reverse_dictionary[valid_examples[i]]
        top_k = 8  # number of nearest neighbors
        nearest = (-sim[i, :]).argsort()[1:top_k + 1]
        log_str = 'Nearest to %s:' % valid_word
        for k in xrange(top_k):
          close_word = reverse_dictionary[nearest[k]]
          log_str = '%s %s,' % (log_str, close_word)
        print(log_str)
  final_embeddings = normalized_embeddings.eval()
  #保存embedding文件
  np.save('embedding.npy', final_embeddings)
# Step 6: Visualize the embeddings.


# pylint: disable=missing-docstring
# Function to draw visualization of distance between embeddings.
#解决负号'-'显示为方块的问题
def plot_with_labels(low_dim_embs, labels, filename):
  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
  plt.figure(figsize=(18, 18))  # in inches
  #plt.rcParams['font.sans-serif']=['Droid Sans Fallback']      #绘图支持中文,指定默认字体
  #plt.rcParams['axes.unicode_minus']=False       #解决负号'-'显示为方块的问题  
  for i, label in enumerate(labels):
    x, y = low_dim_embs[i, :]
    plt.scatter(x, y)
    plt.annotate(label,
                 xy=(x, y),
                 xytext=(5, 2),
                 textcoords='offset points',
                 ha='right',
                 va='bottom',fontproperties=zhfont)

  plt.savefig(filename)

try:
  # pylint: disable=g-import-not-at-top
  from sklearn.manifold import TSNE
  import matplotlib.pyplot as plt

  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
  plot_only = 500#显示词频最高的五百个
  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
  labels = [reverse_dictionary[i] for i in xrange(plot_only)]
  #导出示例图片
  plot_with_labels(low_dim_embs, labels, '/home/ai/work/W11/quiz-w10-code/tsne_w11.png')

except ImportError as ex:
  print('Please install sklearn, matplotlib, and scipy to show embeddings.')
  print(ex)
